Update app.py

app.py

@@ -2,18 +2,19 @@ import os
 import subprocess
 import random
 
-# Install flash attention
+# Install flash attention, skipping CUDA build if necessary
 subprocess.run(
     "pip install flash-attn --no-build-isolation",
     env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
     shell=True,
 )
-
+import requests
+from bs4 import BeautifulSoup
+# Import necessary libraries
 import copy
 import spaces
 import time
 import torch
-
 from threading import Thread
 from typing import List, Dict, Union
 import urllib
@@ -33,14 +34,18 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 from transformers import AutoModel
 from transformers import AutoProcessor
 
+# Load pre-trained models for image captioning and language modeling
 model3 = AutoModel.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)
 processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)
 
+# Define a function for image captioning
 @spaces.GPU(queue=False)
 def videochat(image3, prompt3):
+    # Process input image and prompt
     inputs = processor(text=[prompt3], images=[image3], return_tensors="pt")
+    # Generate captions
     with torch.inference_mode():
-         output = model3.generate(
+        output = model3.generate(
             **inputs,
             do_sample=False,
             use_cache=True,
@@ -48,40 +53,47 @@ def videochat(image3, prompt3):
             eos_token_id=151645,
             pad_token_id=processor.tokenizer.pad_token_id
         )
-    
-    prompt_len = inputs["input_ids"].shape[1]
+        prompt_len = inputs["input_ids"].shape[1]
+    # Decode and return the generated captions
     decoded_text = processor.batch_decode(output[:, prompt_len:])[0]
     if decoded_text.endswith("<|im_end|>"):
         decoded_text = decoded_text[:-10]
     yield decoded_text
 
+# Define Gradio theme
 theme = gr.themes.Soft(
     primary_hue="blue",
     secondary_hue="orange",
     neutral_hue="gray",
-    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif']).set(
-        body_background_fill_dark="#111111",
-        block_background_fill_dark="#111111",
-        block_border_width="1px",
-        block_title_background_fill_dark="#1e1c26",
-        input_background_fill_dark="#292733",
-        button_secondary_background_fill_dark="#24212b",
-        border_color_primary_dark="#343140",
-        background_fill_secondary_dark="#111111",
-        color_accent_soft_dark="transparent")
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif']
+).set(
+    body_background_fill_dark="#111111",
+    block_background_fill_dark="#111111",
+    block_border_width="1px",
+    block_title_background_fill_dark="#1e1c26",
+    input_background_fill_dark="#292733",
+    button_secondary_background_fill_dark="#24212b",
+    border_color_primary_dark="#343140",
+    background_fill_secondary_dark="#111111",
+    color_accent_soft_dark="transparent"
+)
 
+# Set default language for speech recognition
 default_lang = "en"
+# Initialize speech recognition engine
+engines = {default_lang: nemo(default_lang)}
 
-engines = { default_lang: nemo(default_lang) }
-
+# Define a function for speech-to-text transcription
 def transcribe(audio):
     lang = "en"
     model = engines[lang]
     text = model.stt_file(audio)[0]
     return text
 
+# Get Hugging Face API token
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
+# Define a function to get the appropriate InferenceClient based on model name
 def client_fn(model):
     if "Nous" in model:
         return InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
@@ -93,46 +105,50 @@ def client_fn(model):
         return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
     elif "Zephyr" in model:
         return InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-    else: 
+    else:
         return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
+# Define a function to generate a random seed
 def randomize_seed_fn(seed: int) -> int:
     seed = random.randint(0, 999999)
     return seed
 
+# System instructions for the language model
 system_instructions1 = "[SYSTEM] Answer as Real OpenGPT 4o, Made by 'KingNish', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. You will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"
 
+# Define a function for language modeling
 def models(text, model="Mixtral 8x7B", seed=42):
-
     seed = int(randomize_seed_fn(seed))
-    generator = torch.Generator().manual_seed(seed)  
-    
+    generator = torch.Generator().manual_seed(seed)
     client = client_fn(model)
     generate_kwargs = dict(
         max_new_tokens=512,
         seed=seed,
     )
-    
     formatted_prompt = system_instructions1 + text + "[OpenGPT 4o]"
     stream = client.text_generation(
-        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
+    )
     output = ""
     for response in stream:
         if not response.token.text == "</s>":
             output += response.token.text
-
     return output
 
+# Define an asynchronous function to handle voice input and generate responses
 async def respond(audio, model, seed):
     user = transcribe(audio)
     reply = models(user, model, seed)
     communicate = edge_tts.Communicate(reply)
+    # Save the generated speech to a temporary file
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
         tmp_path = tmp_file.name
         await communicate.save(tmp_path)
     yield tmp_path
 
+# Set device to CUDA if available, otherwise CPU
 DEVICE = torch.device("cuda")
+# Load pre-trained models for image-based chat
 MODELS = {
     "idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained(
         "HuggingFaceM4/idefics2-8b-chatty",
@@ -140,28 +156,20 @@ MODELS = {
         _attn_implementation="flash_attention_2",
     ).to(DEVICE),
 }
+# Load pre-trained processor for image-based chat
 PROCESSOR = AutoProcessor.from_pretrained(
     "HuggingFaceM4/idefics2-8b",
 )
 
+# Define system prompt for the image-based chat model
 SYSTEM_PROMPT = [
     {
         "role": "system",
         "content": [
             {
                 "type": "text",
-                "text": """I am OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, I aim to provide an unparalleled experience. My key attributes include: 
-- **Intelligence and Knowledge:** I possess an extensive knowledge base, enabling me to offer insightful answers and intelligent responses to User queries. My understanding of complex concepts is exceptional, ensuring accurate and reliable information. 
-- **Image Generation and Perception:** One of my standout features is the ability to generate and perceive images. Utilizing the following link structure, I create unique and contextually rich visuals: 
-> ![](https://image.pollinations.ai/prompt/{StyleofImage}%20{OptimizedPrompt}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}?width={width}&height={height}&nologo=poll&nofeed=yes&seed={random})
-For image generation, I replace {info inside curly braces} with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. 
-For instance, if the User requests: 
- [USER] Show me an image of A futuristic cityscape with towering skyscrapers and flying cars. 
- [OpenGPT 4o] Generating Image you requested: 
- ![](https://image.pollinations.ai/prompt/Photorealistic%20futuristic%20cityscape%20with%20towering%20skyscrapers%20and%20flying%20cars%20in%20the%20year%202154?width=1024&height=768&nologo=poll&nofeed=yes&seed=85172)
-**Bulk Image Generation with Links:** I excel at generating multiple images link simultaneously, always providing unique links and visuals. I ensure that each image is distinct and captivates the User.
-Note: Make sure to always provide image links starting with ! .As given in examples. 
-My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question.""" },
+                "text": """I am OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, I aim to provide an unparalleled experience. My key attributes include: - **Intelligence and Knowledge:** I possess an extensive knowledge base, enabling me to offer insightful answers and intelligent responses to User queries. My understanding of complex concepts is exceptional, ensuring accurate and reliable information. - **Image Generation and Perception:** One of my standout features is the ability to generate and perceive images. Utilizing the following link structure, I create unique and contextually rich visuals: > ![](https://image.pollinations.ai/prompt/{StyleofImage}%20{OptimizedPrompt}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}?width={width}&height={height}&nologo=poll&nofeed=yes&seed={random})For image generation, I replace {info inside curly braces} with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. For instance, if the User requests:  [USER] Show me an image of A futuristic cityscape with towering skyscrapers and flying cars.  [OpenGPT 4o] Generating Image you requested:  ![](https://image.pollinations.ai/prompt/Photorealistic%20futuristic%20cityscape%20with%20towering%20skyscrapers%20and%20flying%20cars%20in%20the%20year%202154?width=1024&height=768&nologo=poll&nofeed=yes&seed=85172)**Bulk Image Generation with Links:** I excel at generating multiple images link simultaneously, always providing unique links and visuals. I ensure that each image is distinct and captivates the User.Note: Make sure to always provide image links starting with ! .As given in examples. My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question."""
+            },
         ],
     },
     {
@@ -174,7 +182,7 @@ My ultimate goal is to offer a seamless and enjoyable experience, providing assi
         ],
     }
 ]
-
+# Path to example images
 examples_path = os.path.dirname(__file__)
 EXAMPLES = [
     [
@@ -196,7 +204,8 @@ EXAMPLES = [
     [
         {
             "text": "Identify two famous people in the modern world.",
-            "files": [f"{examples_path}/example_images/elon_smoking.jpg", f"{examples_path}/example_images/steve_jobs.jpg",]
+            "files": [f"{examples_path}/example_images/elon_smoking.jpg",
+                      f"{examples_path}/example_images/steve_jobs.jpg", ]
         }
     ],
     [
@@ -226,7 +235,7 @@ EXAMPLES = [
             "text": "What is formed by the deposition of the weathered remains of other rocks?",
             "files": [f"{examples_path}/example_images/ai2d_example.jpeg"],
         }
-    ],    
+    ],
     [
         {
             "text": "What's unusual about this image?",
@@ -235,14 +244,16 @@ EXAMPLES = [
     ],
 ]
 
+# Set bot avatar image
 BOT_AVATAR = "OpenAI_logo.png"
 
+# Chatbot utility functions
 
-# Chatbot utils
+# Check if a turn in the chat history only contains media
 def turn_is_pure_media(turn):
     return turn[1] is None
 
-
+# Load image from URL
 def load_image_from_url(url):
     with urllib.request.urlopen(url) as response:
         image_data = response.read()
@@ -250,7 +261,7 @@ def load_image_from_url(url):
         image = PIL.Image.open(image_stream)
         return image
 
-
+# Convert image to bytes
 def img_to_bytes(image_path):
     image = PIL.Image.open(image_path).convert(mode='RGB')
     buffer = io.BytesIO()
@@ -259,9 +270,9 @@ def img_to_bytes(image_path):
     image.close()
     return img_bytes
 
-
+# Format user prompt with image history and system conditioning
 def format_user_prompt_with_im_history_and_system_conditioning(
-    user_prompt, chat_history
+        user_prompt, chat_history
 ) -> List[Dict[str, Union[List, str]]]:
     """
     Produce the resulting list that needs to go inside the processor. It handles the potential image(s), the history, and the system conditioning.
@@ -273,7 +284,6 @@ def format_user_prompt_with_im_history_and_system_conditioning(
             for content in resulting_message["content"]:
                 if content["type"] == "image":
                     resulting_images.append(load_image_from_url(content["image"]))
-
     # Format history
     for turn in chat_history:
         if not resulting_messages or (
@@ -285,7 +295,6 @@ def format_user_prompt_with_im_history_and_system_conditioning(
                     "content": [],
                 }
             )
-
         if turn_is_pure_media(turn):
             media = turn[0][0]
             resulting_messages[-1]["content"].append({"type": "image"})
@@ -301,7 +310,6 @@ def format_user_prompt_with_im_history_and_system_conditioning(
                     "content": [{"type": "text", "text": user_utterance.strip()}],
                 }
             )
-
     # Format current input
     if not user_prompt["files"]:
         resulting_messages.append(
@@ -311,19 +319,18 @@ def format_user_prompt_with_im_history_and_system_conditioning(
             }
         )
     else:
-        # Choosing to put the image first (i.e. before the text), but this is an arbiratrary choice.
+        # Choosing to put the image first (i.e. before the text), but this is an arbitrary choice.
         resulting_messages.append(
             {
                 "role": "user",
                 "content": [{"type": "image"}] * len(user_prompt["files"])
-                + [{"type": "text", "text": user_prompt["text"]}],
+                          + [{"type": "text", "text": user_prompt["text"]}],
             }
         )
         resulting_images.extend([PIL.Image.open(path) for path in user_prompt["files"]])
-
     return resulting_messages, resulting_images
 
-
+# Extract images from a list of messages
 def extract_images_from_msg_list(msg_list):
     all_images = []
     for msg in msg_list:
@@ -332,80 +339,221 @@ def extract_images_from_msg_list(msg_list):
                 all_images.append(c_)
     return all_images
 
+# List of user agents for web search
+_useragent_list = [
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62',
+    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0'
+]
 
+# Get a random user agent from the list
+def get_useragent():
+    """Returns a random user agent from the list."""
+    return random.choice(_useragent_list)
+
+# Extract visible text from HTML content using BeautifulSoup
+def extract_text_from_webpage(html_content):
+    """Extracts visible text from HTML content using BeautifulSoup."""
+    soup = BeautifulSoup(html_content, "html.parser")
+    # Remove unwanted tags
+    for tag in soup(["script", "style", "header", "footer", "nav"]):
+        tag.extract()
+    # Get the remaining visible text
+    visible_text = soup.get_text(strip=True)
+    return visible_text
+
+# Perform a Google search and return the results
+def search(term, num_results=3, lang="en", advanced=True, sleep_interval=0, timeout=5, safe="active", ssl_verify=None):
+    """Performs a Google search and returns the results."""
+    # Ensure term is a string before parsing
+    if isinstance(term, dict):
+        term = term.get('text', '')  # Get text from user_prompt or default to empty string
+    escaped_term = urllib.parse.quote_plus(term)
+    start = 0
+    all_results = []
+    # Fetch results in batches
+    while start < num_results:
+        resp = requests.get(
+            url="https://www.google.com/search",
+            headers={"User-Agent": get_useragent()},  # Set random user agent
+            params={
+                "q": term,
+                "num": num_results - start,  # Number of results to fetch in this batch
+                "hl": lang,
+                "start": start,
+                "safe": safe,
+            },
+            timeout=timeout,
+            verify=ssl_verify,
+        )
+        resp.raise_for_status()  # Raise an exception if request fails
+        soup = BeautifulSoup(resp.text, "html.parser")
+        result_block = soup.find_all("div", attrs={"class": "g"})
+        # If no results, continue to the next batch
+        if not result_block:
+            start += 1
+            continue
+        # Extract link and text from each result
+        for result in result_block:
+            link = result.find("a", href=True)
+            if link:
+                link = link["href"]
+                try:
+                    # Fetch webpage content
+                    webpage = requests.get(link, headers={"User-Agent": get_useragent()})
+                    webpage.raise_for_status()
+                    # Extract visible text from webpage
+                    visible_text = extract_text_from_webpage(webpage.text)
+                    all_results.append({"link": link, "text": visible_text})
+                except requests.exceptions.RequestException as e:
+                    # Handle errors fetching or processing webpage
+                    print(f"Error fetching or processing {link}: {e}")
+                    all_results.append({"link": link, "text": None})
+            else:
+                all_results.append({"link": None, "text": None})
+        start += len(result_block)  # Update starting index for next batch
+    return all_results
+
+# Format the prompt for the language model
+def format_prompt(user_prompt, chat_history):
+    prompt = "<s>"
+    for item in chat_history:
+        if isinstance(item, tuple):  # Check if it's a text turn
+            prompt += f"[INST] {item[0]} [/INST]"
+            prompt += f" {item[1]}</s> "
+        elif isinstance(item, str):  # Check if it's an image path
+            prompt += f"[INST] <image> [/INST] </s> "  # Placeholder for image turns
+        else: 
+            print(f"Unexpected type in chat_history: {type(item)}") # Debug output
+    prompt += f"[INST] {user_prompt} [/INST]"
+    return prompt
+
+# Define a function for model inference
 @spaces.GPU(duration=30, queue=False)
 def model_inference(
-    user_prompt,
-    chat_history,
-    model_selector,
-    decoding_strategy,
-    temperature,
-    max_new_tokens,
-    repetition_penalty,
-    top_p,
+        user_prompt,
+        chat_history,
+        model_selector,
+        decoding_strategy,
+        temperature,
+        max_new_tokens,
+        repetition_penalty,
+        top_p,
+        web_search,
 ):
-    if user_prompt["text"].strip() == "" and not user_prompt["files"]:
-        gr.Error("Please input a query and optionally an image(s).")
-
-    if user_prompt["text"].strip() == "" and user_prompt["files"]:
-        gr.Error("Please input a text query along with the image(s).")
-
-    streamer = TextIteratorStreamer(
-        PROCESSOR.tokenizer,
-        skip_prompt=True,
-        timeout=120.0,
-    )
-
-    generation_args = {
-        "max_new_tokens": max_new_tokens,
-        "repetition_penalty": repetition_penalty,
-        "streamer": streamer,
-    }
-
-    assert decoding_strategy in [
-        "Greedy",
-        "Top P Sampling",
-    ]
-    if decoding_strategy == "Greedy":
-        generation_args["do_sample"] = False
-    elif decoding_strategy == "Top P Sampling":
-        generation_args["temperature"] = temperature
-        generation_args["do_sample"] = True
-        generation_args["top_p"] = top_p
-
-    # Creating model inputs
-    (
-        resulting_text,
-        resulting_images,
-    ) = format_user_prompt_with_im_history_and_system_conditioning(
-        user_prompt=user_prompt,
-        chat_history=chat_history,
-    )
-    prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True)
-    inputs = PROCESSOR(
-        text=prompt,
-        images=resulting_images if resulting_images else None,
-        return_tensors="pt",
-    )
-    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
-    generation_args.update(inputs)
-
-    thread = Thread(
-        target=MODELS[model_selector].generate,
-        kwargs=generation_args,
-    )
-    thread.start()
-
-    print("Start generating")
-    acc_text = ""
-    for text_token in streamer:
-        time.sleep(0.01)
-        acc_text += text_token
-        if acc_text.endswith("<end_of_utterance>"):
-            acc_text = acc_text[:-18]
-        yield acc_text
-
+    # Define generation_args at the beginning of the function
+    generation_args = {}  
 
+    # Web search logic
+    if not user_prompt["files"]:
+        if web_search is True:
+            """Performs a web search, feeds the results to a language model, and returns the answer."""
+            web_results = search(user_prompt["text"])
+            web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
+            # Load the language model
+            client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
+            generate_kwargs = dict(
+                max_new_tokens=4000,
+                do_sample=True,
+            )
+            # Format the prompt for the language model
+            formatted_prompt = format_prompt(
+                f"""You are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, You are provided with WEB info from which you can find informations to answer. You do not say Unnecesarry things Only say thing which is important and relevant. You also has the ability to generate images. BY Utilizing the following link structure, : ![](https://image.pollinations.ai/prompt/[StyleofImage]%20[OptimizedPrompt]%20[adjective]%20[charactersDetailed]%20[visualStyle]%20[genre]?width=[width]&height=[height]&nologo=poll&nofeed=yes&seed=[random])    For image generation, You replace info inside bracets with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience.     Note: 1. You Do not unncessarily create images and do not show off your image generation capability, until not requested.    2. You always Give image link in format ![](url)    3. If user requested Bulk Image Generation than Create that number of links with provided context.    4. Always learn from previous conversation.    5. Always try to connect conversation with history.    6. Do not make conversation too long.    7. Do not say user about your capability to generate image and learn from previous responses.    My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question.    [USER] {user_prompt} [WEB] {web2} [OpenGPT 4o]""",
+                chat_history)
+            # Generate the response from the language model
+            stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
+                                            return_full_text=False)
+            output = ""
+            # Construct the output from the stream of tokens
+            for response in stream:
+                if not response.token.text == "</s>":
+                    output += response.token.text
+                yield output
+        else:
+            client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+            generate_kwargs = dict(
+                max_new_tokens=5000,
+                do_sample=True,
+            )
+            # Format the prompt for the language model
+            formatted_prompt = format_prompt(
+                f"""You are OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, You do not say Unnecesarry things Only say thing which is important and relevant. You also has the ability to generate images. BY Utilizing the following link structure, : ![](https://image.pollinations.ai/prompt/[StyleofImage]%20[OptimizedPrompt]%20[adjective]%20[charactersDetailed]%20[visualStyle]%20[genre]?width=[width]&height=[height]&nologo=poll&nofeed=yes&seed=[random])    For image generation, You replace info inside bracets with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience.     Note: 1. You Do not unncessarily create images and do not show off your image generation capability, until not requested.    2. You always Give image link in format ![](url)    3. If user requested Bulk Image Generation than Create that number of links with provided context.    4. Always learn from previous conversation.    5. Always try to connect conversation with history.    6. Do not make conversation too long.    7. Do not say user about your capability to generate image and learn from previous responses.    My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question.    [USER] {user_prompt} [OpenGPT 4o]""",
+                chat_history)
+            # Generate the response from the language model
+            stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
+                                            return_full_text=False)
+            output = ""
+            # Construct the output from the stream of tokens
+            for response in stream:
+                if not response.token.text == "</s>":
+                    output += response.token.text
+                yield output
+        return
+    else:
+        if user_prompt["text"].strip() == "" and not user_prompt["files"]:
+            gr.Error("Please input a query and optionally an image(s).")
+            return  # Stop execution if there's an error
+
+        if user_prompt["text"].strip() == "" and user_prompt["files"]:
+            gr.Error("Please input a text query along with the image(s).")
+            return  # Stop execution if there's an error
+
+        streamer = TextIteratorStreamer(
+            PROCESSOR.tokenizer,
+            skip_prompt=True,
+            timeout=120.0,
+        )
+        # Move generation_args initialization here
+        generation_args = {
+            "max_new_tokens": max_new_tokens,
+            "repetition_penalty": repetition_penalty,
+            "streamer": streamer,
+        }
+        assert decoding_strategy in [
+            "Greedy",
+            "Top P Sampling",
+        ]
+
+        if decoding_strategy == "Greedy":
+            generation_args["do_sample"] = False
+        elif decoding_strategy == "Top P Sampling":
+            generation_args["temperature"] = temperature
+            generation_args["do_sample"] = True
+            generation_args["top_p"] = top_p
+        # Creating model inputs
+        (
+            resulting_text,
+            resulting_images,
+        ) = format_user_prompt_with_im_history_and_system_conditioning(
+            user_prompt=user_prompt,
+            chat_history=chat_history,
+        )
+        prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True)
+        inputs = PROCESSOR(
+            text=prompt,
+            images=resulting_images if resulting_images else None,
+            return_tensors="pt",
+        )
+        inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+        generation_args.update(inputs)
+        thread = Thread(
+            target=MODELS[model_selector].generate,
+            kwargs=generation_args,
+        )
+        thread.start()
+        acc_text = ""
+        for text_token in streamer:
+            time.sleep(0.01)
+            acc_text += text_token
+            if acc_text.endswith("<end_of_utterance>"):
+                acc_text = acc_text[:-18]
+            yield acc_text
+        return
+# Define features for the dataset
 FEATURES = datasets.Features(
     {
         "model_selector": datasets.Value("string"),
@@ -416,11 +564,10 @@ FEATURES = datasets.Features(
         "max_new_tokens": datasets.Value("int32"),
         "repetition_penalty": datasets.Value("float32"),
         "top_p": datasets.Value("int32"),
-        }
-    )
-
+    }
+)
 
-# Hyper-parameters for generation
+# Define hyper-parameters for generation
 max_new_tokens = gr.Slider(
     minimum=2048,
     maximum=16000,
@@ -469,22 +616,23 @@ top_p = gr.Slider(
     info="Higher values are equivalent to sampling more low-probability tokens.",
 )
 
-
+# Create a chatbot interface
 chatbot = gr.Chatbot(
     label="OpnGPT-4o-Chatty",
     avatar_images=[None, BOT_AVATAR],
-    show_copy_button=True, 
-    likeable=True, 
+    show_copy_button=True,
+    likeable=True,
     layout="panel"
 )
+output = gr.Textbox(label="Prompt")
 
-output=gr.Textbox(label="Prompt")
+# Create Gradio blocks for different functionalities
 
+# Chat interface block
 with gr.Blocks(
-    fill_height=True,
-    css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
+        fill_height=True,
+        css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
 ) as chat:
-
     gr.Markdown("# Image Chat, Image Generation, Image classification and Normal Chat")
     with gr.Row(elem_id="model_selector_row"):
         model_selector = gr.Dropdown(
@@ -496,17 +644,16 @@ with gr.Blocks(
             label="Model",
             visible=False,
         )
-
     decoding_strategy.change(
         fn=lambda selection: gr.Slider(
             visible=(
-                selection
-                in [
-                    "contrastive_sampling",
-                    "beam_sampling",
-                    "Top P Sampling",
-                    "sampling_top_k",
-                ]
+                    selection
+                    in [
+                        "contrastive_sampling",
+                        "beam_sampling",
+                        "Top P Sampling",
+                        "sampling_top_k",
+                    ]
             )
         ),
         inputs=decoding_strategy,
@@ -517,7 +664,6 @@ with gr.Blocks(
         inputs=decoding_strategy,
         outputs=top_p,
     )
-
     gr.ChatInterface(
         fn=model_inference,
         chatbot=chatbot,
@@ -531,30 +677,34 @@ with gr.Blocks(
             max_new_tokens,
             repetition_penalty,
             top_p,
-        ],   
+            gr.Checkbox(label="Web Search", value=True),  # Add web_search checkbox
+        ],
     )
 
-with gr.Blocks() as voice:   
-     with gr.Row():
-        select = gr.Dropdown([ 'Nous Hermes Mixtral 8x7B DPO', 'Mixtral 8x7B','StarChat2 15b','Mistral 7B v0.3','Phi 3 mini', 'Zephyr 7b' ], value="Mistral 7B v0.3", label="Select Model")
+# Voice chat block
+with gr.Blocks() as voice:
+    with gr.Row():
+        select = gr.Dropdown(['Nous Hermes Mixtral 8x7B DPO', 'Mixtral 8x7B', 'StarChat2 15b', 'Mistral 7B v0.3',
+                              'Phi 3 mini', 'Zephyr 7b'], value="Mistral 7B v0.3", label="Select Model")
         seed = gr.Slider(
-        label="Seed",
-        minimum=0,
-        maximum=999999,
-        step=1,
-        value=0,
-        visible=False
+            label="Seed",
+            minimum=0,
+            maximum=999999,
+            step=1,
+            value=0,
+            visible=False
         )
         input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
         output = gr.Audio(label="AI", type="filepath",
-                        interactive=False,
-                        autoplay=True,
-                        elem_classes="audio")
+                          interactive=False,
+                          autoplay=True,
+                          elem_classes="audio")
         gr.Interface(
-            fn=respond, 
-            inputs=[input, select,seed],
-                outputs=[output], api_name="translate", live=True)
+            fn=respond,
+            inputs=[input, select, seed],
+            outputs=[output], api_name="translate", live=True)
 
+# Live chat block
 with gr.Blocks() as livechat:  
     gr.Interface(
         fn=videochat,